期刊
SENSORS
卷 19, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/s19245363
关键词
Parkinson's disease; machine learning; classification; wearables; accelerometer; GAITRite; multi-regression normalization; SVM; random forest classifier
资金
- Keep Control project, European Union's Horizon 2020 research and innovation ITN program under the Marie Sklodowska-Curie [721577]
- Parkinson's UK [J-0802, G-1301]
- National Institute for Health Research (NIHR) Newcastle Biomedical Research Center (BRC) based at Newcastle Upon Tyne Hospital NHS Foundation Trust and Newcastle University [09/H0906/82]
- NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust
Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway-GAITRite; and an accelerometer attached at the lower back-Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 +/- 7.81% vs. 80.49 +/- 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.
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